Systems for parsing material properties from within shg signals

ABSTRACT

Semiconductor metrology systems based on directing radiation on a wafer, detecting second harmonic generated (SHG) radiation from the wafer and correlating the second harmonic generated (SHG) signal to one or more electrical properties of the wafer are disclosed. The disclosure also includes parsing the SHG signal to remove contribution to the SHG signal from one or more material properties of the sample such as thickness. Systems and methods described herein include machine learning methodologies to automatically classify obtained SHG signal data from the wafer based on an electrical property of the wafer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit under 35 U.S.C. §119(e) of U.S.Provisional Application No. 62/078,636, filed on Nov. 12, 2014, titled“Systems for Parsing Material Properties from Within SHG Signals,” whichis incorporated by reference herein in its entirety, including but notlimited to each of the Sections I, II, III, and IV, of the APPENDIXwhich are each incorporated herein by reference in their entirety.

FIELD

The subject filing relates to systems for wafer inspection,semiconductor metrology, materials characterization, surfacecharacterization and/or interface analysis.

BACKGROUND

In nonlinear optics, light beam input(s) are output as the sum,difference or harmonic frequencies of the input(s). Second HarmonicGeneration (SHG) is a non-linear effect in which light is emitted from amaterial at a reflected angle with twice the frequency of an incidentsource light beam. The process may be considered as the combining of twophotons of energy E to produce a single photon of energy 2E (i.e., theproduction of light of twice the frequency (2ω) or half the wavelength)of the incident radiation. The effect can also be generalized as thecombining of photons of different energies, corresponding to differentfrequencies.

Without subscribing to any particular theory, the SHG process does notoccur within the bulk of materials exhibiting a center of symmetry(i.e., in inversion or centrosymmetric materials). For these materials,the SHG process is appreciable only at surfaces and/or interfaces wherethe inversion symmetry of the bulk material is broken. As such, the SHGprocess offers a unique sensitivity to surface and interface properties.

So-understood, the SHG effect can be useful in detecting interfaceproperties during wafer fabrication in Chemical Vapor Deposition (CVD)processing. Accordingly, SHG techniques can provide a unique non-contactwafer/substrate inspection opportunity.

SUMMARY

The systems employ multiple characterization techniques in one device.Namely, an SHG metrology characterization module is integrated with atleast one secondary analysis device including but not limited to: aspectroscopic ellipsometer (SE), a reflectometer, a CV-IV parametricanalyzer, an Inductively Coupled Plasma Mass Spectrometry (ICPMS), VaporPhase decomposition (VPD)-ICPMS, a Total Reflection X-Ray Fluorescence(TXRF), a Secondary Ion Mass Spectrometry (SIMS), a RutherfordBackscattering (RBS), a Scanning/Tunneling Electron Microscope(SEM/TEM), an Atomic Force Microscope (AFM), a Brightfield/DarkfieldMicroscopy, a Glow Discharge Optical Emission Spectroscopy (GD-OES), anX-Ray Photoelectron Spectroscopy (XPS), a Fourier Transform InfraredSpectroscopy (FTIR), or a microwave detected photoconductive decay(μ-PCD).

One innovative aspect of the subject matter of this disclosure isembodied in a method for characterizing a sample. The method comprisesdirecting a beam of electro-magnetic radiation to a sample using anoptical source; detecting a Second Harmonic Generation (SHG) signalusing an optical detector, wherein the detected SHG signal includes aportion attributed to one or more material properties of the sample;measuring the one or more material properties of the sample using asecondary analysis device; and under control of an electronic processingcircuit: correlating the detected SHG signal with the measured one ormore material properties of the sample; removing the portion attributedto one or more material properties of the sample to obtain a parsed SHGsignal data; and estimating a characteristic of the sample from theparsed SHG signal data.

In various embodiments, the optical source can be a laser. In variousembodiments, the secondary analysis device system can comprise at leastone of: a reflectometer, a spectroscopic ellipsometer (SE), a CV-IVparametric analyzer, an Inductively Coupled Plasma Mass Spectrometry(ICPMS), Vapor Phase decomposition (VPD)-ICPMS, a Total Reflection X-RayFluorescence (TXRF), a Secondary Ion Mass Spectrometry (SIMS), aRutherford Backscattering (RBS), a Scanning/Tunneling ElectronMicroscope (SEM/TEM), Atomic Force Microscope (AFM),Brightfield/Darkfield Microscopy, Glow Discharge Optical EmissionSpectroscopy (GD-OES), X-Ray Photoelectron Spectroscopy (XPS), FourierTransform Infrared Spectroscopy (FTIR), or a microwave detectedphotoconductive decay (μ-PCD). The one or more material properties ofthe sample can include at least one of: thickness of one or more layersof the sample or presence of a known artifact. The characteristic of thesample estimated from the parsed SHG signal data can include one or moreelectrical properties of the sample. The one or more electricalproperties of the sample can include at least one of: local surface andsubsurface metal; organic or inorganic contaminants; trap chargedensity; strain or doping levels. In various embodiments, removing theportion attributed to one or more material properties of the sample cancomprise determining a quantitative relationship between the measuredone or more material properties of the sample and the detected SHGsignal; and adjusting the detected SHG signal by an amount of SHG signalthat is expected from a sample having the measured one or more materialproperties. In various embodiments, adjusting the detected SHG signalcan comprise dividing the detected SHG signal by an amount of SHG signalthat is expected from a sample having the measured one or more materialproperties. In various embodiments, removing the portion attributed toone or more material properties of the sample can comprise determining aquantitative relationship between the measured one or more materialproperties of the sample and the detected SHG signal; and deconvolutingthe detected SHG signal by an amount of SHG signal that is expected froma sample having the measured one or more material properties.

Another innovative aspect of the subject matter of this disclosure isembodied in a system for characterizing a sample. The system comprisesan optical source configured to direct a beam of electro-magneticradiation to a sample; an optical detector configured to detect a SecondHarmonic Generation (SHG) signal, wherein the detected SHG signalincludes a portion attributed to one or more material properties of thesample; a secondary analysis device configured to measure one or morematerial properties of the sample; and an electronic processing circuit.The electronic processing circuit is configured to: correlate thedetected SHG signal with the measured one or more material properties ofthe sample; remove the portion attributed to one or more materialproperties of the sample to obtain a parsed SHG signal data; andestimate a characteristic of the sample from the parsed SHG signal data.

In various embodiments, the secondary analysis device system cancomprise at least one of: a reflectometer, a spectroscopic ellipsometer(SE), a CV-IV parametric analyzer, an Inductively Coupled Plasma MassSpectrometry (ICPMS), Vapor Phase decomposition (VPD)-ICPMS, a TotalReflection X-Ray Fluorescence (TXRF), a Secondary Ion Mass Spectrometry(SIMS), a Rutherford Backscattering (RBS), a Scanning/Tunneling ElectronMicroscope (SEM/TEM), Atomic Force Microscope (AFM),Brightfield/Darkfield Microscopy, Glow Discharge Optical EmissionSpectroscopy (GD-OES), X-Ray Photoelectron Spectroscopy (XPS), FourierTransform Infrared Spectroscopy (FTIR), or a microwave detectedphotoconductive decay (μ-PCD).

In various embodiments, the one or more material properties of thesample includes at least one of: thickness of one or more layers of thesample or presence of a known artifact. In various embodiments, thecharacteristic of the sample estimated from the parsed SHG signal datacan include one or more electrical properties of the sample. The one ormore electrical properties of the sample can include at least one of:local surface and subsurface metal; organic or inorganic contaminants;trap charge density; strain or doping levels. In various embodiments,the electronic processing circuit can be configured to remove theportion attributed to one or more material properties of the sample by:determining a quantitative relationship between the measured one or morematerial properties of the sample and the detected SHG signal; andadjusting the detected SHG signal by an amount of SHG signal that isexpected from a sample having the measured one or more materialproperties.

In various embodiments, adjusting the detected SHG signal can includedividing the detected SHG signal by an amount of SHG signal that isexpected from a sample having the measured one or more materialproperties. In various embodiments, the electronic processing circuitcan be configured to remove the portion attributed to one or morematerial properties of the sample by: determining a quantitativerelationship between the measured one or more material properties of thesample and the detected SHG signal; and deconvoluting the detected SHGsignal by an amount of SHG signal that is expected from a sample havingthe measured one or more material properties.

Another innovative aspect of the subject matter of this disclosure isembodied in an automated method of characterizing electrical propertiesof a sample. The method comprising: receiving a signal from a sample,the signal comprising Second Harmonic Generation (SHG) signal; and underthe control of a hardware computing device: processing the receivedsignal to extract features from the SHG signal related to the electricalproperties of the sample, wherein features from the SHG signal areextracted using a transform; and correlating the extracted features toone or more electrical properties of the sample.

In various embodiments, the extracted features can includespatio-temporal intensity of the SHG signal. In various embodiments, thetransform can comprise at least one of: a Fourier transform, a waveletor a machine learning kernel. In various embodiments, correlating theextracted features can include under the control of the hardwarecomputing device: decoding the extracted features using a decoder;mapping the decoded extracted features onto a decision; and classifyingthe SHG signal based on the decision.

In various embodiments, the decision can include presence or absence ofmetal contaminant. In various embodiments, the decision can include atleast one of: presence of metal contaminant, absence of metalcontaminant, type of contaminant, or amount of metal contaminant. Thedecoder can be a linear or a nonlinear decoder.

In various embodiments, mapping the decoded extracted features onto adecision can comprise projecting the extracted features onto a decisionboundary. The decision boundary can be obtained during a training phaseof the automated system. In various embodiments, the automated methodcan further comprise under the control of the hardware computing device:removing a portion of the SHG signal attributed to one or more materialproperties of the sample. In various embodiments, the portion of the SHGsignal attributed to one or more material properties of the sample canbe removed prior to extracting features from the SHG signal. In variousembodiments, removing the portion of the SHG signal attributed to one ormore material properties of the sample can include receiving dataassociated with one or more material properties of the sample;determining a quantitative relationship between the received dataassociated with one or more material properties of the sample and thereceived signal; and normalizing the received signal to remove theportion of the SHG signal attributed to one or more material propertiesof the sample. In various embodiments, data associated with one or morematerial properties of the sample can be received from using a secondarysemiconductor analysis device.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures schematically illustrate aspects of various embodiments ofdifferent inventive variations.

FIG. 1A is a diagram of an embodiment of a SHG metrology system. FIGS.1B and 1C depict results obtained for a wafer using an embodiment of aSHG metrology system and a reflectometer. The Wafer is uncontaminated200 mm 1500/1000 nm SOT. FIGS. 1B and 1C depict that the layer thicknessof the wafer correlates strongly and directly with the maximum SHGsignal at any given point as observed from the crescent motif of FIG.1C.

FIG. 2 depicts a method of decoupling SHG signal from layer thickness

FIG. 3 illustrates a plot that establishes quantitative relationshipbetween Device Layer Thickness and SHG Max Signal. A 3^(rd) orderpolynomial curve is fit to the obtained Max SHG signal to account forthe curvature of the distribution of the SHG Max Signal with respect tothe Device Layer Thickness. The distribution of the SHG Max Signal withrespect to the Device Layer Thickness is nonlinear as indicated by the3^(rd) order polynomial curve having coefficients a=−1.78e−01,b=8.87e+02, c=−1.47e+06, d=8.16e+08.

FIG. 4A depicts the maximum SHG signal obtained across a wafer using anembodiment of a SHG metrology system and a reflectometer. FIG. 4Bdepicts the device layer thickness across the wafer. FIG. 4C depicts thecorrected maximum SHG signal across the wafer obtained by taking intoaccount variations in the device layer thickness. The corrected maximumSHG signal can be obtained by dividing the maximum SHG signal at eachpoint by expected maximum SHG signal at that point given a thickness ofthe layer at that point.

FIGS. 5A and 5B depict the statistical comparison of the maximum SHGsignal before and after correction. The maps reflect that the wafer isexpected to be without major complications. The maps also reflect thatthe intra-wafer variability due to layer thickness is reduced.

FIGS. 6A-6D depict the maximum SHG signal wafer maps of wafers havingknown levels of surface Cu contamination. FIG. 6A depicts the maximumSHG signal for a control wafer. FIG. 6B depicts the maximum SHG signalfor a 200 mm 1500/1000 nm SOI wafer spin-coated with a Cu contaminationlevel of 1E10 atoms/cm². FIG. 6C depicts the maximum SHG signal for a200 mm 1500/1000 nm SOI wafer spin-coated with a Cu contamination levelof 1E11 atoms/cm². FIG. 6D depicts the maximum SHG signal for a 200 mm1500/1000 nm SOI wafer spin-coated with a Cu contamination level of 1E12atoms/cm².

FIGS. 7A-7D depict the maximum SHG signal maps normalized by taking intoaccount layer thickness effects corresponding to the maps depicted inFIGS. 6A-6C.

FIG. 8 depicts an embodiment of a system architecture comprising a SHGmetrology characterization module that is configured to normalize theSHG signals by taking into account the device layer thickness.

FIG. 9 depicts an embodiment of a system comprising a SHG metrologysystem in conjunction with a reflectometer.

FIG. 10 is a flowchart that illustrates a method of providing an outputfrom an embodiment of a SHG metrology system.

FIG. 11 is a flowchart that depicts a method of providing an output froman embodiment of a SHG metrology system in a supervised training mode.

FIG. 12 is a flowchart that depicts a method of providing an output froman embodiment of a SHG metrology system in a supervised testing mode.

FIG. 13 is a flowchart that depicts a method of providing an output froman embodiment of a SHG metrology system in an unsupervised trainingmode.

FIG. 14 is a flowchart that depicts a method of providing an output froman embodiment of a SHG metrology system in an unsupervised testing mode.

FIG. 15 illustrates a method of classifying obtained SHG signal data.

FIG. 16A illustrates a method of classifying obtained SHG signal datausing an automated hardware system including a linear decoder.

FIG. 16B illustrates a plot of distance to decision boundary of the SHGdata signals projected on the hyperplane w^(T)x obtained during trainingphase of the automated hardware system including a linear decoder.

FIG. 17 illustrates a method of estimating the SHG signal dataclassification accuracy using signal detection theory.

DETAILED DESCRIPTION

FIG. 1A is a diagram of a system 2100 as may employed in connection withthe subject methodology. The system includes a primary laser 2010 fordirecting a primary beam 2012 of electro-magnetic radiation at a samplewafer 2020, which sample is held by a vacuum chuck 2030. The chuck 2030includes or is set on x- and y-stages and optionally also a rotationalstage for positioning a sample site 2022 across the wafer relative towhere the radiation is directed. A beam 2014 of reflected radiationdirected at a detector 2040 will include an SHG signal. The detector maybe any of a photomultiplier tube, a CCD camera, an avalanche detector, aphotodiode detector, a streak camera and a silicon detector. The samplesite 2022 can include one or more layers. The sample site 2022 cancomprise a composite substrate including at least two layers. The samplesite 2022 can include an interface between two dissimilar materials(e.g., between two different semiconductor materials, between twodifferently doped semiconductor materials, between a semiconductor andan oxide, between a semiconductor and a dielectric material, between asemiconductor and a metal, between an oxide and a metal, between a metaland a metal or between a metal and a dielectric).

Various embodiments of the system 2100 can include one or moreshutter-type devices 2050. These are employed as described in connectionwith the methodology below. The type of shutter hardware used willdepend on the timeframe over which the laser radiation is to be blocked,dumped or otherwise directed away from the sample site.

An electro-optic blocking device such as a Pockel's Cell or Kerr Cellcan be used to obtain very short blocking periods (i.e., with switchingtimes on the order of 10-9 to 10-12 seconds). For longer blocking timeintervals (e.g., from about 10-5 seconds and upwards) mechanicalshutters or flywheel chopper type devices may be employed.

Electro-optic blocking devices can provide a wider range of materials tobe tested in accordance with the methods below. A photon counting system2044 capable of discretely gating very small time intervals, typically,on the order of picoseconds to microseconds can be also be included toresolve the time-dependent signal counts.

In various embodiments of the system 2100 an additional radiation source(for example, a laser illustrated emitting a directed beam or a UV flashlamp emitting a diverging or optically collimated or a focused pulse)may also be incorporated in the system 2100 to provide such features asreferenced above in connection with the portion of U.S. ProvisionalApplication No. 61/980,860, filed on Apr. 17, 2014, titled “WaferMetrology Technologies,” referred to as Section I entitled “Pump andProbe Type SHG Metrology,” which is incorporated herein by reference inits entirety and/or initial charging/saturation in the methods below.See also co-pending U.S. patent application Ser. No. 14/690, 179, filedApr. 17, 2015 titled “Pump and Probe Type Second Harmonic GenerationMetrology”, which is incorporated herein by reference in its entirety.

Various other hardware devices and systems can be used to push themethods into faster-yet time frames. For example, various embodiments ofthe system 2100 can include delay line hardware. The delay line can be avariable delay line which can advantageously allow multiple transientcharge decay interrogation events on a time frame ranging fromimmediately (although delay of only 10⁻¹² seconds may be required formany methodologies) to tens of nanoseconds. In some embodiments, beamsplitting and switching (or shuttering on/off) between a plurality ofset-time delay lines can be used to allow a number of time-delayedinterrogation events.

In various embodiments of the system 2100, the beam 2012 from the laser2010 can be split by a beam splitter between two optical paths. The beamsplitter can be configured to split the beam 2012 unequally between thetwo optical paths. For example, 70% of the energy of the beam 2012 canbe directed along a first optical path (and 30% of the energy of thebeam 2012 can be directed along a second optical path. As anotherexample, 60% of the energy of the beam 2012 can be directed along thefirst optical path and 40% of the energy of the beam 2012 can bedirected along the second optical path. As yet another example, 80% ofthe energy of the beam 2012 can be directed along the first optical pathand 20% of the energy of the beam 2012 can be directed along the secondoptical path. The beam splitter can comprise a dielectric mirror, asplitter cube, a metal coated mirror, a pellicle mirror or a waveguidesplitter. In implementations, where the beam 2012 includes opticalpulses, the beam splitter can include an optical component havingnegligible dispersion that splits the beam 2012 between two opticalpaths such that optical pulses are not broadened. The beam travellingalong one of the first or the second optical paths can be configured asa pump beam and the other can be configured as a probe beam. In thoseembodiments in which the beam splitter is configured to split the beam2012 unequally between the first and the second optical paths, the beamhaving a larger amount of optical energy can be configured as the pumpbeam and the beam having a smaller amount of optical energy can beconfigured as the probe beam. The optical path along which the probebeam travels can be lengthened or shortened to change its arrival timingrelative to the pump beam. In various embodiments, fiber optics can beemployed in the first or the second optical paths to introduce opticaldelay between the pump and the probe beams (e.g., as presented in U.S.Pat. No. 6,819,844 incorporated herein by reference in its entirety forsuch description). In various embodiments, the first and the secondoptical paths can be angled with respect to each other such that thepump and probe beams are incident on the sample wafer at differentangles. Such an approach can facilitate measuring pump and probe SHGresponses separately. In such cases, two detectors may be advantageouslyemployed for detecting SHG responses from the pump and the probe beams.

Referring to FIG. 1A, the output from the detector 2040 and/or thephoton counting system 2044 can be input to an electronic device 2048.The electronic device 2048 can be, for example, a computing device, acomputer, a tablet, a microcontroller or a FPGA. The electronic device2048 can include a processor or processing electronics that may beconfigured to execute one or more software modules. In addition toexecuting an operating system, the processor may be configured toexecute one or more software applications, including a web browser, atelephone application, an email program, or any other softwareapplication. The electronic device 2048 can implement the methodsdiscussed herein by executing instructions included in amachine-readable non-transitory storage medium, such as a RAM, ROM,EEPROM, etc. The electronic device 2048 can include a display deviceand/or a graphic user interface to interact with a user. The electronicdevice 2048 can communicate with one or more devices over a networkinterface. The network interface can include transmitters, receiversand/or transceivers that can communicate over wired or wirelessconnections.

The system 2100 can include one or more optional optical components. Forexample, the system 2100 is shown including a dichroic reflective orrefractive filter 2080 for selectively passing the SHG signal coaxialwith reflected radiation directly from the laser 2010. Alternatively, aprism may be employed to differentiate the weaker SHG signal from themany-orders-of-magnitude-stronger reflected primary beam. Other optionsinclude the use of diffraction grating or a Pellicle beam splitter. Asshown in system 2100, an optical bundle 2082 of focusing andcollimating/collimation optics may be provided. In various embodimentsof the system 2100 additional optical components, such as for exampleone or more optical filters, zoom lens and/or polarizers may beincluded. Also, an angular (or arc-type) rotational adjustment (withcorresponding adjustment for the detector 2040 and in-line opticalcomponents) can also be included in some embodiments.

Referring to the system 2100, laser 2010 may operate in a wavelengthrange between about 700 nm to about 2000 nm with a peak power betweenabout 10 kW and 1 GW, but delivering power at an average below about 100mW. In various embodiments, average powers between 10 mW and 10W shouldbe sufficient. In embodiments including an additional light source(e.g., another laser or a flash lamp) configured as a pump source mayoperate in a wavelength range between about 80 nm and about 800 nmdelivering an average power between about 10 mW and 10 W. Values outsidethese ranges, however, are possible.

In various embodiments, since an SHG signal is weak compared to thereflected beam that produces it, it may be desirable to improve thesignal-to-noise ratio of SHG counts. As photon counting gate timesdecrease for the blocking and/or delay processes described herein,improvement becomes even more useful. One method of reducing noise thatmay be employed is to actively cool the detector. The cooling candecreases the number of false-positive photon detections that aregenerated randomly because of thermal noise. This can be done usingcryogenic fluids such as liquid nitrogen or helium or solid statecooling through use of a Peltier device. Others areas of improvement mayinclude use of a Marx Bank Circuit (MBC) as relevant to shutter speed.

A SHG metrology characterization module uses inputs from the ancillarytechniques to parse material properties via physically derived machinelearning models from within the measured SHG signal, while providing asmaller footprint, reducing cost, and increasing throughput. The systemallows for the extraction of independent semiconductor and materialproperties from the unparsed SHG signals. Stated otherwise, embodimentshereof employ integration of additional characterization techniques suchas those aforementioned with the non-destructive characterizationabilities of SHG metrology, enhanced by a suite of physically derivedmachine learning models to interpret SHG signals as a portfolio ofindependent wafer properties, such as layer material layer thicknessvariation, defect and contaminant.

SHG based metrology systems can be useful for measuring semiconductorwafer parameters, such as but not limited to local surface andsubsurface metal and organic contamination, trap charge density, strain,and doping levels. Sets of samples have been made with controlled levelsof contaminant or defect in order to assess and verify the sensitivityof SHG. SHG signal level shows clear contrast between controlled samplesin this context.

However, SHG readings from existing SHG based metrology systems canbecome much more complicated when evaluating real-world samples withunknown types and levels of defect and material properties. For example,it presently involves substantial expert interpretation based onmeasured SHG signal alone to estimate whether a variation in SHG signalacross a wafer is due to an electrical defect or a material propertyvariation. To improve materials failure analysis and select correctwafers for further processing, additional (oftentimes destructive)efforts are undertaken to parse SHG signals.

In developing a system to parse material properties from within themeasured SHG signal, the effect of the material properties of the samplewafer (e.g. thickness of one or more layers of the sample wafer) on theSHG signal was unexpectedly discovered. It was observed that variationsin the thickness of one or more layers of the sample wafer canoftentimes camouflage SHG signal changes from industrially relevantcontamination and wafer defect, leading to false positive identificationof industrially problematic material. It was observed that SHG signalvariance from acceptable layer variations can be on the same order ofmagnitude as signal variance from unacceptable levels of materialcontamination or defect. Unparsed monitoring of an SHG signal across amaterial with no consideration for layer thickness variations, can makeit difficult to detect and differentiate changes in SHG signal caused bylayer thickness variations versus changes in SHG signal due toindustrially relevant levels of electrically active contamination orstructural defect.

As an example, a batch of four SOI wafers from a leading manufacturerwere selected for intentional contamination, to show the efficacy of SHGin characterizing varying levels of surface metals. When the wafers werecharacterized via SHG, there was a much larger variance of SHG signalwithin each wafer than between the wafers leading to ambiguity betweenthe samples. Four wafers were used: one as a control, and threecontaminated at levels of 1E10, 1E11, and 1E12 atoms Cu/cm2respectively. As seen in FIG. 1B, relying on unparsed SHG signal levelsmade it difficult to tell good from bad wafers.

When measurements had first been taken and analyzed, it was thought thatthe experiment was a failure. As a result of this problem, the ensuingwork directed towards parsing SHG signals uncovered that the experimentwas not a failure, but that another undetected problem existed.

Information provided by the vendor regarding the sample wafers that weretested indicated a uniform device layer thickness of 1500 nanometers.However, when additional measurements of the sample wafer obtained usinga secondary analysis device including but not limited to: aspectroscopic ellipsometer (SE), a reflectometer, a CV-IV parametricanalyzer, an Inductively Coupled Plasma Mass Spectrometry (ICPMS), VaporPhase decomposition (VPD)-ICPMS, a Total Reflection X-Ray Fluorescence(TXRF), a Secondary Ion Mass Spectrometry (SIMS), a RutherfordBackscattering (RBS), a Scanning/Tunneling Electron Microscope(SEM/TEM), an Atomic Force Microscope (AFM), a Brightfield/DarkfieldMicroscopy, a Glow Discharge Optical Emission Spectroscopy (GD-OES), anX-Ray Photoelectron Spectroscopy ( )PS), a Fourier Transform InfraredSpectroscopy (FTIR), or a microwave detected photoconductive decay(μ-PCD) indicated that the device layer was not uniform, as seen in FIG.1C. In various embodiments, the secondary analysis device can beseparate from and/or distinct from the SHG metrology system. Upon thegeneration of extensive layer thickness maps and side-by-side comparisonwith SHG map, the strong influence of layer thickness variation on SHGmeasurement was discovered. For example, both the device layer thicknessmap of FIG. 1C and the SHG signal map of FIG. 1B indicate a crescentshape. This discovery was unexpected, as it was extraordinary that sucha large SHG variance was being caused by a variance in layer thickness.

Specifically, the side-by-side comparisons of the SHG signal map and thedevice layer thickness map indicated that variations of only 10 nm in a1500 nm thick device layer (less than 0.7% variance) are correlated witha more than 30% difference in SHG signal levels. This is a very largechange that is not expected or easily explained by simple thin filminterference effects with the 800 nm fundamental signal.

Indeed, layer thickness variations made it impractical to note thedifference between wafer samples contaminated with copper atconcentrations of 1E+10 and 1E+11 atoms/cm2 on the surface, and acontrol wafer with no copper added as evident in comparing FIGS. 6A-6D.However, as shown in FIGS. 7A-7D, once the samples were normalized forlayer thickness effects the differences between the varying contaminantlevels and control sample became readily visible and statisticallyrelevant.

As noted in FIG. 2, SHG signal from a sample wafer obtained using a SHGmetrology system can include contributions from electrical properties ofthe sample wafer including but not limited to local surfacecontamination, local subsurface contamination, trap density and/orcharge carrier information. Additionally, as discussed above, the SHGsignal also includes contribution from one or more material propertiesincluding but not limited to variation in thickness of one or morelayers of the sample wafer and/or presence of a known artifact. Inaddition, the SHG signal can also include contribution from structuraldefects. The embodiments disclosed herein are configured to parse theobtained SHG signal to remove contribution from one or more materialproperties of the sample wafer including but not limited to variation inthickness of one or more layers of the sample wafer and/or presence of aknown artifact and isolating the contribution to SHG signal fromelectrical properties of the sample wafer and/or structural defects.

Such “parsed” or “de-cloaked” SHG measurements enabled distinguishingbetween the levels of contamination on wafers that would fail in laterprocessing steps versus good wafers, while providing the benefit oflayer thickness measurements simultaneously through SHG.

Accordingly, the problem of SHG signal parsing or defect de-cloaking caninvolve integrating information about layer thickness and the othertechniques referenced alongside the spatio-temporal SHG signal. Thesubject embodiments thereby address the challenge of signalinterpretation by including additional characterization techniquesinside the SHG metrology module, and using physically derived machinelearning models to parse the SHG signal based on correspondingmeasurements. This ability can be extended to other material propertiesand analysis techniques, as described below including: SE/reflectometry,CV-IV, TXRF, Vapor Phase decomposition (VPD)-ICPMS, μ-PCD, RutherfordBackscattering (RBS), a Scanning/Tunneling Electron Microscope(SEM/TEM), Atomic Force Microscope (AFM), Brightfield/DarkfieldMicroscopy, Glow Discharge Optical Emission Spectroscopy (GD-OES), X-RayPhotoelectron Spectroscopy ( )PS), Fourier Transform InfraredSpectroscopy (FTIR), and time of flight (TOF)-SIMS that are based onmetrology which are widely used in semiconductor fabrication plants,commonly referred to as fabs.

The subject method proceeds by first performing measurements on the testsample in question (a “scan”) using the integrated hardware. Thesemeasurements could then be used in conjunction with a suite ofmathematical algorithms to interpret unprocessed SHG measurements.

FIG. 8 illustrates the overall system architecture. SHG signals areacquired using the hardware platform and analyzed in a computer using asuite of machine learning tools. Signals using other traditionalmetrology tools can enhance the performance of system as tutor signalsfor the machine learning algorithms. Over a term of use, correlating theresults of SHG metrology with these tools will enable the SHG metrologytool to “learn” characteristics in the SHG signal counts pertaining tocommercially relevant parameters.

In one variation, as shown in FIG. 9, a reflectometer is used inconjunction with the SHG apparatus to identify and isolate the portionof SHG signal variation that is due to layer thickness variations inmultilayer materials. The SHG and layer thickness maps are combinedusing a physically derived machine learning model to learn what thenormal contribution of layer thickness is for a given material system.Embodiments of the system illustrated in FIGS. 1A and/or FIG. 9 canadditionally or alternatively include hardware computing devices thatuse embodiments of the machine learning methodologies and techniquesdescribed herein and illustrated in FIGS. 11-17 to, for example, parseor classify SHG signal data based on electrical properties of the samplewafer.

For 1500/1000 nm SOI material, reflectometer measurements were comparedto SHG measurements at identical points across sample wafers, and aquantitative relationship deduced between the device layer thickness andthe maximum SHG signal obtained at each of these identical points isshown in FIG. 3.

The quantitative relationship demonstrated in FIG. 3 was used to dividethe SHG signal at each point by the expected SHG given the device layerthickness in order to control for the layer thickness effects andproduce an SHG mapping of the wafer from which material thickness hasbeen removed as a variable. This approach allows further signal analysismethods to determine electrical properties of the material from the SHGsignal without the camouflaging effect of layer thickness described inthe problem statement. As automated by virtue of physically derivedmachine learning models, materials can be characterized by correlationwith any secondary analysis technique with minimal user input.

Such physically derived machine learning models concern the followingaspects of wafer properties, although is not limited thereto: layerthickness, artifact detection, artifact identification and artifactquantification. In addition, the commercial need for in-line tools alsorequires efficient computational algorithms to achieve in-situ resultsfor materials characterization.

FIG. 10 depicts an SHG characterization module. The general architecturefor the machine learning comprises a training module and testing module.The training module takes SHG signals and tutor signals (optional) asinput, depending on whether the machine learning technique is supervisedor unsupervised. The training module generates output that isconsequently evaluated using the signal detection theory, which measuresthe performance of the training module. After reaching certain level ofsatisfactory performance, new SHG signals can be passed through thetesting module, which gives out a predicted layer thickness and/orinformation about wafer defects. Two types of training modules aredescribed on the basis of whether the selected module is for supervisedlearning or unsupervised learning.

FIG. 11 illustrates a supervised learning mode, where both SHG signalsand tutor signals are fed into the SHG characterization module as input.Within the SHG characterization module, an iterative algorithm isemployed and numerical optimization is required. A forward model isdevised to describe the relationship between SHG signals and groundtruth response, such as layer thickness and metal defect, which can beextracted by tutor signals. A loss function is constructed based on thediscrepancy between the predicted response and the ground truthresponse. A numerical optimization is performed through an iterativeprocedure to obtain the desirable model parameters that minimize theloss function. Stopping criteria are employed to terminate theoptimization procedure. Once optimal solution for the model parametersis reached, the model parameters can be output for the testing mode. Asshown in FIG. 12, under the supervised testing mode, the testing modulegets input from the optimal model parameters and new SHG signals, andparses out information pertaining to wafer properties as output, such aslayer thickness and various defects.

FIG. 13 illustrates the unsupervised learning mode, where the trainingmodule takes only SHG signals as input. An iterative procedure isadministrated, where a forward model is devised and the ultimate goal isto identify region of interest (ROI) and signal characteristics (such assingle curve characterization), while correlation between SHG signalsand other destructive signals becomes the judge for the learning system.As shown in FIG. 14, under the unsupervised testing mode, one uses theidentified ROI and characteristics to parse information on new SHGsignals.

Notably, wafer properties can be roughly categorized into threecategories: layer thickness, artifact detection and artifactidentification. The machine learning methodologies for each category maydiffer as discussed below.

Layer Thickness:

Supervised learning may be used to characterize layer thickness. SHGsignals will be measured on samples with known thickness, whereas theground truth for layer thickness can be obtained via other existingtechniques. Supervised learning algorithms will be used to map out thefunction between input (SHG signals) and output (layer thickness). Suchsupervised learning algorithms include linear regression, nonlinearregression, and neural network. Choices of algorithms will depend on thenature and manufacturer of different wafers. The supervised learningarchitecture is trained until it reaches certain accuracy, which can bequantified using signal detection theory, for instance the receiveroperator characteristic (ROC) curve. After that, layer thickness can bepredicted based on SHG signals measured on new samples.

Artifact Detection:

SHG signals may be collected from control wafers (without artifact) andtarget wafers (with artifact) for training purposes. Features will beextracted from the SHG signals using a variety of transforms, includingbut not limited to, using the original signal, Fourier transform,wavelet, kernel-based methods, a machine learning kernel (e.g., a Fisherkernel, a graph kernel, a polynomial kernel, a RBF kernel, a stringkernel) or any feature extraction technique. A sparse logisticregression and/or sparse support vector machine will be employed tocorrelate the extracted features with wafer conditions (artifact ornot). The learned weights are stored in the computer for futureprediction. Once the training reaches certain accuracy, SHG signals arecollected for the new wafer and prediction made as to whether or not ithas an artifact based on a forward model that uses the learned weightsand input SHG signals.

Artifact Identification:

A series of wafer samples may be identified and constructed based onindustrial needs, which have certain known artifacts. Information aboutthe artifact type and spatial location will be obtained. SHG signalswill be measured on these samples. Similarly to artifact detection,features will be extracted from the SHG signals using a variety oftransforms, including but not limited to, using the original signal,Fourier transform, wavelet, kernel-based methods, a machine learningkernel (e.g., a Fisher kernel, a graph kernel, a polynomial kernel, aRBF kernel, a string kernel) or any feature extraction technique. Amultinomial training model will be employed to accommodate differenttypes of artifact, and a sparse logistic regression and/or sparse vectormachine will be trained using the multinomial model. Such a model willlearn a mapping from input (SHG signals) to output (artifact type). Thelearned weights will be stored in the computer for future prediction.Once the training reaches certain accuracy, SHG signals may be collectedfor the new wafer and prediction of the specific artifact type based ona forward model that uses the learned weights and input SHG signals.

Finally, a portfolio of machine learning methodologies (per a ForwardModel Methodology) targeted for wafer characterization herein or herebyis summarized below:

Loss Function Machine Learning Layer Thickness Square norm RidgeRegression Square norm LASSO Square norm Neural network ContaminationLogistic loss L2-regularized Detection logistic regression Logistic lossL1-regularized logistic regression Hinge loss Support vector machineContamination Multinomial logistic loss Multinomial Identificationlogistic regression Multi-class hinge loss Multi-class support vectormachine Zero one loss Deep learning

In accordance with these variations and others as described above, manyimplementations are possible according to devices, systems, methods(including software and associated hardware for carrying out specifiedacts) and UI features (including layouts and options and/or methodologyassociated with system use).

In various embodiments, machine learning methodologies can be employedto classify parsed SHG signal maps. Automated hardware systems can beemployed to classify various parsed SHG signal maps based on thepresence or absence of contaminants, the amount of contaminants or otherelectrical and structural characteristics that may be relevant from anindustrial perspective. Embodiments of such automated hardware systemscan be additionally or alternatively be included with embodiments ofsemiconductor metrology based systems and devices including but notlimited to embodiments of the systems illustrated in FIGS. 1A and 9. Invarious embodiments, depending on the composition of the sample wafer,parsing of the SHG signal obtained from the wafer may be omitted and theobtained SHG signal can be used by the automated hardware systems toclassify the sample wafer. For example, parsing of the obtained SHGsignal to remove contributions to the SHG signal from variations inthickness of one or more layers can be omitted if the sample wafercomprises a bulk material, such as, for example, Si. However, if thesample wafer comprises hetero-interface materials, such as, for exampleSOI, then the obtained SHG signal can be parsed to remove contributionsto the SHG signal from variations in thickness of one or more layers asdiscussed above. For example, SHG signal from hetero-interfacematerials, such as, for example SOI, can be parsed to remove layerthickness effects by (a) collecting layer thickness data using acorrelative technique such as, for example, surface reflectivity (SR) orSE; and (b) establishing a mathematical relationship between theobtained SHG signal and the layer thickness data (e.g., a 3^(rd) orderpolynomial curve).

Classifying a SHG signal map can include extracting features from theSHG signal map; decoding the extracted features using a decoder; makinga decision based on the output of the decoder; and classifying the SHGsignal map based on the decision. This method is illustrated in FIG. 15.

The parsed (or unparsed) SHG signal output for various portions of thesample wafer (also referred to herein as SHG signal map) can beprocessed to extract features. For example, the SHG signal map can befed through certain types of kernels to extract features. For example,features can be extracted by using a variety of transforms including butnot limited to using the original signal, Fourier transform, wavelet,kernel-based methods, a machine learning kernel (e.g., a Fisher kernel,a graph kernel, a polynomial kernel, a RBF kernel, a string kernel) orany feature extraction technique. Depending on the type of kernel, theextracted features can be a feature vector (as shown in FIG. 15) or afeature matrix. In various embodiments, such features can bespatial-temporal intensity of the SHG signal. In another embodiment, thefeatures can be based on or extracted from a Fourier transform of theSHG signal. A decoder is applied on the aforementioned extractedfeatures in order to map the signal to the decision, the decision beingthe label of the data. For example, such a decision can encode whetheror not the wafer has metal contamination. The decoder can be eitherlinear or nonlinear, depending on the applications. The decoder can betrained based on an ensemble of SHG signals together with traininglabels (also referred to herein as ground truth). The decoded extractedfeatures (also referred to herein as testing data) can be projected ontoa decision boundary obtained from training the decoder. By projectingthe decoded extracted features a decision which corresponds to thetraining labels can be made. The SHG signal map can be classified basedon the decision. For example, in some embodiments, the training labelcan be presence or absence of metal contamination in the sample wafer.In such embodiments, based on the decoding of the extracted features adecision can be made whether the obtained SHG signal map should beclassified as having metal contamination or not having metalcontamination.

Signal detection theory can be used to quantify classification accuracyin terms of true positive rate vs. false positive rate. Standard crossvalidation can be used to assess the classification accuracy. In variousembodiments, different models including but not limited to logisticregression, L1-regularized logistic regression, support vector machine,sparse support vector machine, neural network or deep learning anddifferent solvers including but not limited to iterative shrinkage,gradient descent, interior point method, hybrid iterative shrinkage orlinearized Bregman can be employed to train the decoder.

FIG. 16A illustrates a method of classifying the obtained SHG signal mapusing an automated hardware device including a linear decoder. In someembodiments, the automated hardware device can be used to train thesystem to classify obtained SHG signal maps according to particularquantity and/or species of metal contaminants as compared to a controlsample. In various embodiments, the obtained SHG signal maps can beclassified by taking into account and deconvoluting in a quantifiedfashion structural aspects such as layer thickness variation. In variousembodiments, the linear decoder can be modeled as L1-regularizedlogistic regression, which automatically selects features that areinformative about the decision boundary that can be geometricallyinterpreted as a hyperplane in the feature space.

As discussed above, the features extracted from the obtained SHG signalmaps are projected onto a decision boundary obtained from training theautomated system. For the automated system including a linear decoder,the projection onto a decision boundary includes projection onto atrained hyperplane w^(T)x. The decision boundary obtained from trainingthe automated system including a linear decoder is indicated by thesolid line in FIG. 16B and the closed circles indicate the projecteddata. A decision can be made based on the distance from the decisionboundary.

In various embodiments, the obtained SHG signal data can be mapped intoa projection vector, which clusters the obtained SHG signals into two ormore decisions. Signal detection theory can be used to estimate the SHGsignal data classification accuracy, as shown in FIG. 17. For example,signal detection theory can be used to visualize true positive rate(TPR) vs false positive rate (FPR). The area under the receiver operatorcharacteristic (ROC) curve can be used to determine the classificationaccuracy.

As a further example an automated system configured to parse SHG signaldata to extract features from the SHG signal data and correlate theextracted features to one or more electrical properties of a samplewafer (e.g., presence or absence of contaminants, amount and/or speciesof contaminants, etc.) can be configured to perform one or more of thefollowing operations. The automated system can be configured to removelayer thickness effects if the sample wafer comprises a heterointerfacematerial, such as, for example SOI. The layer thickness effects can beremoved by using data received from a secondary analysis device, suchas, for example, a spectroscopical ellipsometer (SE), a reflectometer, aCV-IV parametric analyzer, an Inductively Coupled Plasma MassSpectrometry (ICPMS), Vapor Phase decomposition (VPD)-ICPMS, a TotalReflection X-Ray Fluorescence (TXRF), a Secondary Ion Mass Spectrometry(SIMS), a Rutherford Backscattering (RBS), a Scanning/Tunneling ElectronMicroscope (SEM/TEM), Atomic Force Microscope (AFM),Brightfield/Darkfield Microscopy, Glow Discharge Optical EmissionSpectroscopy (GD-OES), X-Ray Photoelectron Spectroscopy ( )PS), FourierTransform Infrared Spectroscopy (FTIR), or a microwave detectedphotoconductive decay (μ-PCD). The secondary analysis device can beseparate from and distinct from the SHG metrology system. The system canbe configured to feed the SHG signal with or without the layer thicknesseffects removed through a kernel (e.g., a machine learning kernel) toextract features. The extracted features can be decoded using a decoder.The decoder can be a linear or a nonlinear decoder. The decoder can betrained using supervised or unsupervised training methods. For example,in some implementations a linear decoder can be trained based on anensemble of SHG signals together with training labels (ground truth). Totrain the linear decoder different models as well as different solverscan be used to efficiently solve for the different models. The differentmodels can include, Logistic regression, L1-regularized logisticregression, Support vector machine (SVM), sparse support vector machine,Neural network and/or Deep learning. The different solvers can includeHybrid iterative shrinkage and/or Linearized Bregman approaches.

The automated system can be configured to project the decoded featuresfrom the SHG signal data (also referred to as testing data) onto adecision boundary obtained from training. The SHG signal data can beclassified based on the decision. The automated system can be configuredto use signal detection theory to quantify classification accuracy interms of true positive rate vs false positive rate. In some embodiments,the automated system can be configured to use standard cross validationto access the ultimate classification accuracy

Various embodiments described herein provide unique ability inisolating, controlling for, and measuring semiconductor materialproperties. Various embodiments described herein concern a hardwaresystem for generating SHG signal combined with complementary techniques,as well as a suite of machine learning methods for analyzing SHG signalsrelative to the complementary techniques. SHG signal—including its timedependence—conveys information on a plurality of material propertiesincluding but not limited to layer thickness, trap density, localsurface contamination and subsurface contamination. The subject systemsenable extracting individual material parameters.

Various embodiments, together with details regarding a selection offeatures have been set forth above. As for other details, these may beappreciated in connection with the above-referenced patents andpublications as well as is generally known or appreciated by those withskill in the art. The same may hold true with respect to method-basedaspects of the disclosure in terms of additional acts as commonly orlogically employed. Regarding such methods, including methods ofmanufacture and use, these may be carried out in any order of the eventswhich is logically possible, as well as any recited order of events.Furthermore, where a range of values is provided, it is understood thatevery intervening value, between the upper and lower limit of that rangeand any other stated or intervening value in the stated range isencompassed. Also, it is contemplated that any optional feature of theinventive variations described may be set forth and claimedindependently, or in combination with any one or more of the featuresdescribed herein.

Though various embodiments have been described in reference to severalexamples, optionally incorporating various features, they are not to belimited to that which is described or indicated as contemplated withrespect to each such variation. Changes may be made to any of theembodiments described and equivalents (whether recited herein or notincluded for the sake of some brevity) may be substituted withoutdeparting from the true spirit and scope hereof.

The various illustrative processes described may be implemented orperformed with a general purpose processor, a Digital Signal Processor(DSP), an Application Specific Integrated Circuit (ASIC), a FieldProgrammable Gate Array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. The processor can be partof a computer system that also has a user interface port thatcommunicates with a user interface, and which receives commands enteredby a user, has at least one memory (e.g., hard drive or other comparablestorage, and random access memory) that stores electronic informationincluding a program that operates under control of the processor andwith communication via the user interface port, and a video output thatproduces its output via any kind of video output format, e.g., VGA, DVI,HDMI, DisplayPort, or any other form.

A processor may also be implemented as a combination of computingdevices, e.g., a combination of a DSP and a microprocessor, a pluralityof microprocessors, one or more microprocessors in conjunction with aDSP core, or any other such configuration. These devices may also beused to select values for devices as described herein.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.A software module may reside in Random Access Memory (RAM), flashmemory, Read Only Memory (ROM), Electrically Programmable ROM (EPROM),Electrically Erasable Programmable ROM (EEPROM), registers, hard disk, aremovable disk, a CD-ROM, or any other form of storage medium known inthe art. An exemplary storage medium is coupled to the processor suchthat the processor can read information from, and write information to,the storage medium. In the alternative, the storage medium may beintegral to the processor. The processor and the storage medium mayreside in an ASIC. The ASIC may reside in a user terminal. In thealternative, the processor and the storage medium may reside as discretecomponents in a user terminal.

In one or more exemplary embodiments, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on, transmittedover or resulting analysis/calculation data output as one or moreinstructions, code or other information on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. The memory storagecan also be rotating magnetic hard disk drives, optical disk drives, orflash memory based storage drives or other such solid state, magnetic,or optical storage devices.

Also, any connection is properly termed a computer-readable medium. Forexample, if the software is transmitted from a website, server, or otherremote source using a coaxial cable, fiber optic cable, twisted pair,digital subscriber line (DSL), or wireless technologies such asinfrared, radio, and microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technologies such as infrared,radio, and microwave are included in the definition of medium. Disk anddisc, as used herein, includes compact disc (CD), laser disc, opticaldisc, digital versatile disc (DVD), floppy disk and Blu-ray disc wheredisks usually reproduce data magnetically, while discs reproduce dataoptically with lasers. Combinations of the above should also be includedwithin the scope of computer-readable media.

Operations as described herein can be carried out on or over a website.The website can be operated on a server computer, or operated locally,e.g., by being downloaded to the client computer, or operated via aserver farm. The website can be accessed over a mobile phone or a PDA,or on any other client. The website can use HTML code in any form, e.g.,MHTML, or HML, and via any form such as cascading style sheets (“CSS”)or other.

Also, the inventors hereof intend that only those claims which use thewords “means for” are to be interpreted under 35 USC 112, sixthparagraph. Moreover, no limitations from the specification are intendedto be read into any claims, unless those limitations are expresslyincluded in the claims. The computers described herein may be any kindof computer, either general purpose, or some specific purpose computersuch as a workstation. The programs may be written in C, or Java, Brewor any other programming language. The programs may be resident on astorage medium, e.g., magnetic or optical, e.g. the computer hard drive,a removable disk or media such as a memory stick or SD media, or otherremovable medium. The programs may also be run over a network, forexample, with a server or other machine sending signals to the localmachine, which allows the local machine to carry out the operationsdescribed herein.

It is also noted that all features, elements, components, functions,acts and steps described with respect to any embodiment provided hereinare intended to be freely combinable and substitutable with those fromany other embodiment. If a certain feature, element, component,function, or step is described with respect to only one embodiment, thenit should be understood that that feature, element, component, function,or step can be used with every other embodiment described herein unlessexplicitly stated otherwise. This paragraph therefore serves asantecedent basis and written support for the introduction of claims, atany time, that combine features, elements, components, functions, andacts or steps from different embodiments, or that substitute features,elements, components, functions, and acts or steps from one embodimentwith those of another, even if the following description does notexplicitly state, in a particular instance, that such combinations orsubstitutions are possible. It is explicitly acknowledged that expressrecitation of every possible combination and substitution is overlyburdensome, especially given that the permissibility of each and everysuch combination and substitution will be readily recognized by those ofordinary skill in the art.

In some instances entities are described herein as being coupled toother entities. It should be understood that the terms “interfit”,“coupled” or “connected” (or any of these forms) may be usedinterchangeably herein and are generic to the direct coupling of twoentities (without any non-negligible, e.g., parasitic, interveningentities) and the indirect coupling of two entities (with one or morenon-negligible intervening entities). Where entities are shown as beingdirectly coupled together, or described as coupled together withoutdescription of any intervening entity, it should be understood thatthose entities can be indirectly coupled together as well unless thecontext clearly dictates otherwise.

Reference to a singular item includes the possibility that there are aplurality of the same items present. More specifically, as used hereinand in the appended claims, the singular forms “a,” “an,” “said,” and“the” include plural referents unless specifically stated otherwise. Inother words, use of the articles allow for “at least one” of the subjectitem in the description above as well as the claims below.

It is further noted that the claims may be drafted to exclude anyoptional element (e.g., elements designated as such by descriptionherein a “typical,” that “can” or “may” be used, etc.). Accordingly,this statement is intended to serve as antecedent basis for use of suchexclusive terminology as “solely,” “only” and the like in connectionwith the recitation of claim elements, or other use of a “negative”claim limitation language. Without the use of such exclusiveterminology, the term “comprising” in the claims shall allow for theinclusion of any additional element—irrespective of whether a givennumber of elements are enumerated in the claim, or the addition of afeature could be regarded as transforming the nature of an element setforth in the claims. Yet, it is contemplated that any such “comprising”term in the claims may be amended to exclusive-type “consisting”language. Also, except as specifically defined herein, all technical andscientific terms used herein are to be given as broad a commonlyunderstood meaning as possible while maintaining claim validity.

While the embodiments are susceptible to various modifications andalternative forms, specific examples thereof have been shown in thedrawings and are herein described in detail. It should be understood,however, that these embodiments are not to be limited to the particularform disclosed, but to the contrary, these embodiments are to cover allmodifications, equivalents, and alternatives falling within the spiritof the disclosure. Furthermore, any features, functions, acts, steps, orelements of the embodiments may be recited in or added to the claims, aswell as negative limitations (as referenced above, or otherwise) thatdefine the inventive scope of the claims by features, functions, steps,or elements that are not within that scope. Thus, the breadth of thevariations or the inventive concepts are not to be limited to theexamples provided, but only by the scope of the claim language tofollow.

What is claimed is:
 1. A method for characterizing a sample, the methodcomprising: directing a beam of electro-magnetic radiation to a sampleusing an optical source; detecting a Second Harmonic Generation (SHG)signal using an optical detector, wherein the detected SHG signalincludes a portion attributed to one or more material properties of thesample; measuring the one or more material properties of the sampleusing a secondary analysis device; and under control of an electronicprocessing circuit: correlating the detected SHG signal with themeasured one or more material properties of the sample; removing theportion attributed to one or more material properties of the sample toobtain a parsed SHG signal data; and estimating a characteristic of thesample from the parsed SHG signal data.
 2. The method of claim 1,wherein the optical source is a laser.
 3. The method of claim 1, whereinthe secondary analysis device system comprises at least one of: areflectometer, a spectroscopic ellipsometer (SE), a CV-IV parametricanalyzer, an Inductively Coupled Plasma Mass Spectrometry (ICPMS), VaporPhase decomposition (VPD)-ICPMS, a Total Reflection X-Ray Fluorescence(TXRF), a Secondary Ion Mass Spectrometry (SIMS), a RutherfordBackscattering (RBS), a Scanning/Tunneling Electron Microscope(SEM/TEM), Atomic Force Microscope (AFM), Brightfield/DarkfieldMicroscopy, Glow Discharge Optical Emission Spectroscopy (GD-OES), X-RayPhotoelectron Spectroscopy (XPS), Fourier Transform InfraredSpectroscopy (FTIR), or a microwave detected photoconductive decay(μ-PCD).
 4. The method of claim 1, wherein the one or more materialproperties of the sample includes at least one of: thickness of one ormore layers of the sample or presence of a known artifact.
 5. The methodof claim 1, wherein the characteristic of the sample estimated from theparsed SHG signal data includes one or more electrical properties of thesample.
 6. The method of claim 5, wherein the one or more electricalproperties of the sample includes at least one of: local surface andsubsurface metal; organic or inorganic contaminants; trap chargedensity; strain or doping levels.
 7. The method of claim 1, whereinremoving the portion attributed to one or more material properties ofthe sample comprises: determining a quantitative relationship betweenthe measured one or more material properties of the sample and thedetected SHG signal; and adjusting the detected SHG signal by an amountof SHG signal that is expected from a sample having the measured one ormore material properties.
 8. The method of claim 7, wherein adjustingthe detected SHG signal comprises dividing the detected SHG signal by anamount of SHG signal that is expected from a sample having the measuredone or more material properties.
 9. The method of claim 1, whereinremoving the portion attributed to one or more material properties ofthe sample comprises: determining a quantitative relationship betweenthe measured one or more material properties of the sample and thedetected SHG signal; and deconvoluting the detected SHG signal by anamount of SHG signal that is expected from a sample having the measuredone or more material properties.
 10. A system for characterizing asample, comprising: an optical source configured to direct a beam ofelectro-magnetic radiation to a sample; an optical detector configuredto detect a Second Harmonic Generation (SHG) signal, wherein thedetected SHG signal includes a portion attributed to one or morematerial properties of the sample; a secondary analysis deviceconfigured to measure one or more material properties of the sample; andan electronic processing circuit configured to: correlate the detectedSHG signal with the measured one or more material properties of thesample; remove the portion attributed to one or more material propertiesof the sample to obtain a parsed SHG signal data; and estimate acharacteristic of the sample from the parsed SHG signal data.
 11. Thesystem of claim 10, wherein the secondary analysis device systemcomprises at least one of: a reflectometer, a spectroscopic ellipsometer(SE), a CV-IV parametric analyzer, an Inductively Coupled Plasma MassSpectrometry (ICPMS), Vapor Phase decomposition (VPD)-ICPMS, a TotalReflection X-Ray Fluorescence (TXRF), a Secondary Ion Mass Spectrometry(SIMS), a Rutherford Backscattering (RBS), a Scanning/Tunneling ElectronMicroscope (SEM/TEM), Atomic Force Microscope (AFM),Brightfield/Darkfield Microscopy, Glow Discharge Optical EmissionSpectroscopy (GD-OES), X-Ray Photoelectron Spectroscopy (XPS), FourierTransform Infrared Spectroscopy (FTIR), or a microwave detectedphotoconductive decay (μ-PCD).
 12. The system of claim 10, wherein theone or more material properties of the sample includes at least one of:thickness of one or more layers of the sample or presence of a knownartifact.
 13. The system of claim 10, wherein the characteristic of thesample estimated from the parsed SHG signal data includes one or moreelectrical properties of the sample.
 14. The system of claim 13, whereinthe one or more electrical properties of the sample includes at leastone of: local surface and subsurface metal; organic or inorganiccontaminants; trap charge density; strain or doping levels.
 15. Thesystem of claim 10, wherein the electronic processing circuit isconfigured to remove the portion attributed to one or more materialproperties of the sample by: determining a quantitative relationshipbetween the measured one or more material properties of the sample andthe detected SHG signal; and adjusting the detected SHG signal by anamount of SHG signal that is expected from a sample having the measuredone or more material properties.
 16. The system of claim 15, whereinadjusting the detected SHG signal comprises dividing the detected SHGsignal by an amount of SHG signal that is expected from a sample havingthe measured one or more material properties.
 17. The system of claim10, wherein the electronic processing circuit is configured to removethe portion attributed to one or more material properties of the sampleby: determining a quantitative relationship between the measured one ormore material properties of the sample and the detected SHG signal; anddeconvoluting the detected SHG signal by an amount of SHG signal that isexpected from a sample having the measured one or more materialproperties.
 18. An automated method of characterizing electricalproperties of a sample, the method comprising: receiving a signal from asample, the signal comprising Second Harmonic Generation (SHG) signal;and under the control of a hardware computing device: processing thereceived signal to extract features from the SHG signal related to theelectrical properties of the sample, wherein features from the SHGsignal are extracted using a transform; and correlating the extractedfeatures to one or more electrical properties of the sample.
 19. Theautomated method of claim 18, wherein the extracted features includespatio-temporal intensity of the SHG signal.
 20. The automated method ofclaim 18, wherein the transform comprises at least one of: a Fouriertransform, a wavelet or a machine learning kernel.
 21. The automatedmethod of claim 18, wherein correlating the extracted featurescomprises: under the control of the hardware computing device: decodingthe extracted features using a decoder; mapping the decoded extractedfeatures onto a decision; and classifying the SHG signal based on thedecision.
 22. The automated method of claim 21, wherein the decisionincludes presence or absence of metal contaminant.
 23. The automatedmethod of claim 21, wherein the decision includes at least one of:presence of metal contaminant, absence of metal contaminant, type ofcontaminant, or amount of metal contaminant.
 24. The automated method ofclaim 21, wherein the decoder is a linear decoder.
 25. The automatedmethod of claim 21, wherein the decoder is a nonlinear decoder.
 26. Theautomated method of claim 21, wherein mapping the decoded extractedfeatures onto a decision comprises projecting the extracted featuresonto a decision boundary.
 27. The automated method of claim 26, whereinthe decision boundary is obtained during a training phase of theautomated system.
 28. The automated method of claim 18, furthercomprising: under the control of the hardware computing device: removinga portion of the SHG signal attributed to one or more materialproperties of the sample.
 29. The automated method of claim 28, whereinthe portion of the SHG signal attributed to one or more materialproperties of the sample is removed prior to extracting features fromthe SHG signal.
 30. The automated method of claim 29, wherein removingthe portion of the SHG signal attributed to one or more materialproperties of the sample comprises: receiving data associated with oneor more material properties of the sample; determining a quantitativerelationship between the received data associated with one or morematerial properties of the sample and the received signal; andnormalizing the received signal to remove the portion of the SHG signalattributed to one or more material properties of the sample.
 31. Theautomated method of claim 30, wherein data associated with one or morematerial properties of the sample is received from using a secondarysemiconductor analysis device.